FutureSmart AI Demos
Server Details
Search evidence-backed AI-tool reviews, rankings, use cases, comparisons & toolkits (read-only).
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Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 13 of 16 tools scored. Lowest: 3.1/5.
Each tool targets a distinct resource or action (compare, get, list, search), with no overlapping purposes. For example, compare_tools is for comparisons, get_evidence for evidence retrieval, and each get_* function retrieves a specific entity type.
Tool names follow a consistent verb_noun pattern using snake_case (e.g., get_tool, list_tools, compare_tools). Even the few noun_preposition_noun names like rankings_for_tool are predictable and fit the pattern.
16 tools cover the full scope of browsing, searching, and retrieving details for AI tools, comparisons, rankings, personas, and use cases. The number is well-balanced—enough to be comprehensive without being overwhelming.
The tool set provides full CRUD-like coverage for browsing the demo catalog (list, get, search, compare). A minor gap is the lack of a direct tool-to-use-case link (e.g., use_cases_for_tool), but the core workflows are complete.
Available Tools
16 toolscompare_toolsBInspect
Evidence-aligned comparison of two tools, honesty enforced structurally: head_to_head (cells from the SAME test input — provable same-input comparison), related_not_same_input (same dimension, different runs — flagged), and each tool's unique evidence. Built from real observation cells with artifacts, not prose.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_a | Yes | First tool slug, e.g. "llamaparse". | |
| tool_b | Yes | Second tool slug, e.g. "landing-ai". | |
| criterion | No | Optional: restrict to one criterion. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must convey behavior. It mentions structural honesty and comparison types (head_to_head vs related_not_same_input) but lacks details on side effects, error handling, or authorization needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose and includes jargon ('head_to_head', 'cells', 'artifacts') that could confuse agents. Multiple sentences could be consolidated for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters, no output schema, and no annotations. The description explains comparison types but omits required parameters explicitly, expected output format, and error scenarios, leaving gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The description adds context about tool slugs and optional criterion but does not significantly enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs an evidence-aligned comparison of two tools, using verb 'comparison' and resource 'tools'. It distinguishes from siblings like 'get_tool' by focusing on pairwise comparison, though phrasing is somewhat complex.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for comparing tools with evidence) but does not explicitly state when not to use it or provide alternatives like 'get_tool' or 'search'. Usage guidance is implied but not structured.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_evidenceAInspect
Query the evidence graph: observation cells (tool × test-scenario × criterion) → verdict, score, the researcher's note, and the REAL artifacts (input/output screenshots) that prove it. The ground truth behind every ranking — filter any combination of tool(s), scenario (slug, group tag, or name), criterion, verdict, or evidence state. evidence_state: "verified" = artifact-backed, "observed" = noted without artifact, "scored-only" = number only.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | Tool slug or name, e.g. "landing-ai". | |
| limit | No | Max cells (default 50). | |
| tools | No | Several tools at once. | |
| ranking | No | Ranking page slug — scope cells to the evidence run that published ranking is bound to (exactly what the page renders). | |
| verdict | No | ||
| evidence | No | ||
| scenario | No | Scenario slug, cross-run group tag (e.g. "scanned-research-paper"), or name fragment. | |
| criterion | No | Criterion slug or name, e.g. "table extraction". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the read-only nature (query), the structure of returned data, and explains the evidence_state enum values. Missing details on error handling or pagination behavior, but still above average.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (three sentences) with no redundant information. Each sentence adds value: first states purpose and output, second ties to rankings, third clarifies evidence states. Well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 8 optional parameters and no output schema or annotations, the description adequately explains the tool's purpose, filters, and special parameter values. It could be more explicit about the return format (e.g., array of cells), but covers enough for effective usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The description adds value by explaining how scenario accepts slugs, group tags, or name fragments, and defines evidence_state values. This extra context enriches parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Query') and the resource ('evidence graph'), and specifies the output (verdict, score, note, artifacts). It distinguishes itself from siblings by focusing on observation cells and evidence, unlike ranking or comparison tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving evidence behind rankings, but does not explicitly state when to use this tool versus alternatives like get_ranking or compare_tools. No exclusions or when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_personaAInspect
Given a persona slug (from list_personas), return everything tagged with it: persona, use_cases[], rankings[], compares[], toolkits[], tools[]. Mirrors a persona landing page.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Persona slug, e.g. "students". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations present, but description transparently describes output structure. Implicitly a read-only operation; no behavioral quirks discussed beyond what's returned. Adequate for simple retrieval.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences with front-loaded purpose. Every word adds value; no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single simple parameter and no output schema, the description covers return structure and usage context. Could mention error handling or pagination but not necessary for tool simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and description adds context: slug is from list_personas with example 'students'. Enhances understanding beyond schema's minimal description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool returns everything tagged with a persona slug, listing specific fields (persona, use_cases[], etc.). Distinguishes from siblings by referencing list_personas as prerequisite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions using list_personas to obtain slugs, and states it mirrors a persona landing page, providing context for use. No explicit alternatives or when-not-to-use, but guidance is clear for a retrieval tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_rankingAInspect
Full ranking detail as a JSON+Markdown envelope: ranked tools (rank/badge/scores), criteria, winner, breakdown (JSON) + verdicts / final take (Markdown). null if unknown. Pass fields to project to only the keys you need (token-efficient).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Ranking slug, e.g. "resume-parsing-api" (from list_rankings). | |
| fields | No | Optional projection: return ONLY these top-level fields (identity id/name/title/slug/url is always included) to control response size. Available: id, title, slug, url, abstract, read_time, tested_date, category, use_case, personas, tags, winner, methodology, tools, breakdown, final_take, evidence_run. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: returns a JSON+Markdown envelope, returns null if unknown, and the projection behavior with always-included identity fields. No annotations are provided, but the description covers the main behavioral traits for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences that cover purpose, return format, and a key usage tip (fields projection). No unnecessary words, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains the return format and handles the null case. Parameters are well-documented with examples. It does not detail the Markdown content, but the tool's nature (read operation) doesn't require extensive disclosure. The mention of `list_rankings` provides helpful context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by providing an example for `slug` and explaining the `fields` projection, including that identity fields are always included and listing available options. This exceeds the schema's enum list.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'Full ranking detail as a JSON+Markdown envelope' with specific components like ranked tools, criteria, winner, and breakdown. It distinguishes from sibling tools like `list_rankings` and `rankings_for_tool` by focusing on retrieving a single ranking's details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for getting ranking details but does not explicitly state when to use this tool versus alternatives like `compare_tools` or `tools_in_ranking`. It provides a usage tip for the `fields` parameter but lacks guidance on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_toolAInspect
Full tool detail as a JSON+Markdown envelope: identity, pricing, per-feature scores, fit, FAQ, relationships (JSON) + our_take / in-depth review (Markdown). null if unknown. Pass fields to project to only the keys you need (token-efficient).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Tool seo_slug, e.g. "affinda" (from list_tools). | |
| fields | No | Optional projection: return ONLY these top-level fields (identity id/name/title/slug/url is always included) to control response size. Available: id, name, slug, url, heading, website, domain, category, sub_category, content_type, personas, tags, rating, testing_history, features, pricing, fit, use_case_track_record, related_pages, related_reads, similar_tools, faq, demo_video, our_take, in_depth_review_md. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It states the tool returns 'null if unknown', indicating error handling for invalid slugs. It does not cover other behavioral aspects like authorization, rate limits, or data freshness, but it is adequate for a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, efficiently front-loading the core purpose and key details. Every part adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (many fields, no output schema), the description covers the main return format, the purpose of the `fields` projection, and null on unknown. It omits detailed error messages or partial data scenarios, but is otherwise complete for a retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds value by explaining the return structure ('JSON+Markdown envelope') and the purpose of the `fields` parameter for projection, which goes beyond the schema's enum list.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns 'Full tool detail' as a JSON+Markdown envelope, specifying the content (identity, pricing, scores, etc.). This distinguishes it from siblings like list_tools which return a list. The verb 'get' matches the retrieval of a single tool by slug.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using the `fields` parameter for token-efficient projection, which is helpful. It implies usage for retrieving detailed tool information, but does not explicitly exclude situations or compare with alternatives like list_tools or compare_tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_use_caseAInspect
Full use-case detail as a JSON+Markdown envelope: step guide, pros/cons, FAQ, tools used (JSON) + the full narrative guide (Markdown, full_md). null if unknown. Pass fields to project to only the keys you need (token-efficient).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Use-case slug (from list_use_cases). | |
| fields | No | Optional projection: return ONLY these top-level fields (identity id/name/title/slug/url is always included) to control response size. Available: id, title, slug, url, abstract, read_time, category, audience, personas, tags, tools_used, step_guide, what_to_expect, faq, full_md. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes the output format and that null is returned for unknown slugs, but lacks details on rate limits, auth requirements, or side effects. It adequately conveys the tool is read-only but implicitly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is two sentences, front-loads the key output format, and immediately addresses the optional parameter. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description effectively describes the return structure (JSON+Markdown envelope with specific sections like step_guide, full_md). It covers null behavior. Could be more precise on JSON envelope structure, but sufficient for a retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters. Description adds value by explaining slug comes from list_use_cases and clarifying projection behavior for fields, including always-included identity fields. This assists the agent in proper invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool returns full use-case detail as a JSON+Markdown envelope including step guide, pros/cons, FAQ, tools used, and full narrative guide. It differentiates from sibling tools like list_use_cases (which lists) and get_tool (different resource).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explains using the `fields` parameter for token-efficient projection. While it does not explicitly state when to avoid this tool, the context of returning detailed data is clear and distinguishes from listing tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
List the category vocabulary in use with per-type counts and a source flag ("collection"|"derived"), sorted by total.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses output structure (per-type counts, source flag, sorting) and implies a read-only operation, but lacks details on pagination, limits, or error states.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no wasted words. Front-loaded with the action and key output features.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description adequately covers the tool's behavior: it lists categories with counts, source flag, sorted by total. Could mention if there are any default limits or if the list is exhaustive, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline is 4. The description correctly adds no parameter details, as none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists category vocabulary with per-type counts and a source flag, sorted by total. It uses a specific verb ('List') and resource ('category vocabulary'), distinguishing it from siblings like list_tools or list_use_cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The purpose is implied, but there is no mention of exclusions or specific contexts (e.g., when to use list_categories vs. list_tools).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_comparesBInspect
List published head-to-head comparison pages: id, title, slug, url, tool_a, tool_b, personas[], shared_use_cases[], updated_at.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max items to return. Omit for all. | |
| offset | No | Items to skip (paging). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits. It does not state whether the operation is read-only, has side effects, or requires authentication.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently states the purpose and lists the output fields, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lists output fields but lacks information on ordering, default behavior, or pagination details beyond what the schema provides. For a simple list tool, it is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both limit and offset. The description adds no additional meaning beyond the schema, earning the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists published head-to-head comparison pages and enumerates the fields returned. This distinguishes it from sibling tools like compare_tools, which likely performs a comparison rather than listing them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as get_evidence or search. The description only states what it does without providing context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_personasAInspect
List personas with published-page counts per type {use_cases, rankings, tools, compares, toolkits}. The slugs are valid input for get_persona.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full behavioral transparency. It explains the return structure (counts per type) but does not mention whether the operation is read-only, any authentication needs, or potential errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences that convey essential information without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description adequately covers what the tool returns and provides a cross-reference to get_persona. However, it does not mention whether results are complete or if there are any limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and the schema description coverage is 100%. Per guidelines, 0 parameters gives a baseline of 4, and the description adds no extra parameter info because none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states that the tool lists personas with published-page counts per type, and notes that the slugs are valid input for get_persona. This clearly distinguishes it from sibling tools like get_persona.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a usage hint by indicating that output slugs can be used with get_persona. While it does not explicitly state when not to use it, the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_rankingsAInspect
List published ranking pages ("best X"): id, title, slug, url, use_case, persona, category, tools_count, winner, tested_as_of, updated_at.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max items to return. Omit for all. | |
| offset | No | Items to skip (paging). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description lacks behavioral details such as pagination behavior, sorting order, or safety guarantees. It mentions 'published' but does not clarify if unpublished rankings are excluded. The description adds minimal behavioral context beyond the basic listing action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently states the action and lists the returned fields. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the number of sibling tools (13) and the simplicity of the tool (listing), the description covers the essential output fields. However, it could mention that the result is an array or provide a hint about default ordering. Still, it is largely sufficient for a listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for limit and offset parameters. The description does not add any additional meaning beyond what the schema provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action 'list' and the resource 'published ranking pages'. It enumerates specific fields returned (id, title, slug, url, use_case, persona, category, tools_count, winner, tested_as_of, updated_at), distinguishing it from sibling tools like 'get_ranking' which returns a single ranking.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. While context from sibling tools implies that this lists all rankings, the description does not state exclusions or comparative use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_toolkitsAInspect
List published toolkit pages (curated bundles): id, title, slug, url, category.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max items to return. Omit for all. | |
| offset | No | Items to skip (paging). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context by stating that it lists 'published' pages, implying a read-only operation. However, with no annotations, it does not explicitly disclose safety or side effects, though the behavior is straightforward.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that efficiently conveys the tool's purpose and output fields without any waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is mostly complete for a list operation: it states the output fields. However, given many sibling tools, it lacks differentiation from list_tools and list_categories, and no output schema exists to compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description does not add meaning beyond the schema's parameter descriptions (limit and offset). The baseline of 3 applies as the schema already fully documents parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action (list), resource (published toolkit pages), and the fields returned (id, title, slug, url, category). It distinguishes from sibling tools like list_tools by specifying 'curated bundles'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus siblings like list_tools, list_categories, or search. The description does not mention use cases, prerequisites, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_toolsBInspect
List published AI tool pages: id, name, slug, url, domain, personas[], categories[].
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max items to return. Omit for all. | |
| offset | No | Items to skip (paging). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of transparency. It does not disclose pagination behavior (default limits, sorting order), rate limits, or whether the operation is read-only. The description only lists output fields without behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the tool's purpose and output. It is front-loaded with the verb 'list' and resource, and uses a colon to enumerate fields without wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 optional params, no output schema), the description is somewhat complete but lacks details on sorting order, default limit, and output types. It partially describes the output but could be more informative for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description is not required to add parameter details. It does not mention limit or offset, which is acceptable since the schema already explains them. The description adds no additional semantic meaning beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'list' and resource 'published AI tool pages', and clearly enumerates the returned fields (id, name, slug, url, domain, personas, categories). This distinguishes it from sibling tools like get_tool (single item) and list_categories (different resource).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. With many sibling list_* tools and compare_tools, the agent lacks context on when to select list_tools over others. No exclusion criteria or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_use_casesBInspect
List published use-case pages (how-to guides): id, title, slug, url, persona, category, updated_at.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max items to return. Omit for all. | |
| offset | No | Items to skip (paging). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that only published pages are listed and enumerates returned fields. With no annotations provided, it carries full burden, but it omits information about authentication requirements, rate limits, or whether the response is paginated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no redundant words. It front-loads the key action and resource, making it efficient for an AI agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with two parameters and no output schema, the description covers the return fields. However, it could be improved by explicitly stating that it returns a list of objects (implicit from field listing) and clarifying pagination defaults.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already has full coverage of both parameters (limit, offset) with descriptions. The tool description adds no additional parameter information, so it is adequate but does not enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action (List) and resource (published use-case pages) and specifies the returned fields. However, it does not differentiate from sibling list tools like list_categories or list_personas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., get_use_case for a single page, or search for filtering). The agent receives no context about appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rankings_for_toolAInspect
Given a tool id, return the tool {id,name,slug} and every ranking it appears in [{id,title,slug,url,use_case,rank,badge}].
| Name | Required | Description | Default |
|---|---|---|---|
| tool_id | Yes | Tool id (from list_tools). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It fully describes the return structure, which is a read operation. No side effects or permissions are mentioned, but for a simple query this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that packs all necessary information (input, output structure) without any extraneous words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one parameter, no output schema required), the description fully covers what the tool does and what it returns. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by linking the parameter to list_tools and by implying its use in fetching the tool and rankings. It provides context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('return') and the resources (tool with specific fields and associated rankings with detailed fields). It distinguishes itself from siblings like get_tool (which likely returns tool details without rankings) and tools_in_ranking (which returns tools for a ranking).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying the input as 'tool id (from list_tools)', suggesting a prerequisite. However, it lacks explicit when-not or alternative guidance. The purpose is clear enough that an agent can infer appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspect
Search the published catalogue across use cases, rankings, tools, comparisons, and toolkits. Returns ranked light refs [{ kind, id, title, slug, url, snippet, score, meta }] — then call get_tool / get_ranking / get_use_case for full detail. mode: keyword (substring), semantic (meaning, via embeddings — finds pages by what they cover), or hybrid (default, fuses both). Optional filters: type[], persona, category.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Search mode. | |
| type | No | Restrict to these kinds (default: all). | |
| limit | No | Max results (default 20). | |
| query | Yes | Free-text search query. | |
| persona | No | Restrict to items tagged with this persona slug. | |
| category | No | Restrict to this category (case-insensitive). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the return format (ranked light refs with fields like kind, id, title, etc.), explains the three search modes (keyword, semantic, hybrid), and lists optional filters. It does not describe side effects, but search is inherently read-only and non-destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise: three sentences that clearly state the purpose, return format, and key parameters. There is no redundant or unnecessary text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has 6 parameters, all with schema descriptions, and no output schema, the description adequately explains the return value structure and suggests subsequent tools for full details. This is complete enough for an agent to correctly invoke and use the results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage for parameters is 100%, meaning each parameter has a description in the schema. The tool description adds value beyond the schema by explaining the semantics of 'mode' (keyword, semantic, hybrid) and how 'type' restricts results. The description does not repeat the schema but enhances understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it searches the published catalogue across use cases, rankings, tools, comparisons, and toolkits, which is a specific verb+resource. It distinguishes from sibling tools like get_tool, get_ranking, etc. by noting that those are for full detail after getting light refs from search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use search (to find items across multiple content types) and distinguishes it from sibling tools by suggesting calling get_tool, get_ranking, or get_use_case for full detail. It doesn't explicitly state when not to use it, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tools_in_rankingAInspect
Given a ranking id, return the ranking {id,title,slug} and its ranked tools [{id,name,slug,url,rank,badge}] (badge: Best/Usable/Needs work/Unstable/Failed).
| Name | Required | Description | Default |
|---|---|---|---|
| ranking_id | Yes | Ranking id (from list_rankings). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the burden of transparency. It explicitly lists the output fields for both the ranking and tools, including the badge enum values. There is no contradiction with annotations (none exist).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys all necessary information without redundancy. Every part of the sentence is informative and adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and absence of output schema, the description provides complete context: the input source, the exact output structure with field names and badge values. No additional information is needed for proper usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, describing the parameter. The description adds value by noting the parameter's origin ('from list_rankings'), which goes beyond the schema and aids the agent in properly using the tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: given a ranking id, return the ranking and its ranked tools with specified fields. It distinguishes from sibling tools like 'get_ranking' (returns only ranking) and 'rankings_for_tool' (returns rankings for a tool), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions that the ranking id comes from 'list_rankings', providing context. However, it lacks explicit guidance on when to use this tool versus alternatives, such as when to choose 'get_ranking' or 'rankings_for_tool'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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